Regularized Latent Class Model for Joint Analysis of High-Dimensional Longitudinal Biomarkers and a Time-to-Event Outcome.
Regularized Latent Class Model for Joint Analysis of High-Dimensional Longitudinal Biomarkers and a Time-to-Event Outcome.
复制标题
用于高维纵向生物标志物和事件时间结果联合分析的正则化潜在类模型。
DOI：
10.1111/biom.12964
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发表时间：
2019
期刊：
影响因子：
1.9
通讯作者：
Zhao,Hongyu
中科院分区：
文献类型：
--
作者：
Sun,Jiehuan;Herazo-Maya,JoseD;Molyneaux,PhilipL;Maher,TobyM;Kaminski,Naftali;Zhao,Hongyu
虽然已经开发了许多建模方法来联合分析纵向生物标记物和事件发生时间结果，但大多数方法只能处理一个或几个生物标记物。在本文中，我们提出了一种新的联合潜在类模型来处理高维纵向生物标志物。我们的模型有三个组件：类成员模型、生存子模型和纵向子模型。在我们的模型中，我们假设协变量可以潜在地影响生物标记物和类成员资格。我们采用惩罚似然方法来推断哪些协变量对生物标记物具有随机效应和/或固定效应，哪些协变量对潜在类具有信息性。通过大量的仿真研究表明，与其他联合建模方法相比，我们提出的方法在预测和将对象分配到正确的类方面具有更好的性能，并且Bootstrap可以用于对我们的模型进行推理。然后，我们将我们的方法应用于特发性肺纤维化患者的数据集，对其基因表达谱进行纵向测量。我们能够识别出四个有趣的潜在类，其中一个类的死亡风险比其他类高得多。我们还发现，每个潜在的类别在某些基因中都有独特的轨迹，产生了新的生物学见解。
Although many modeling approaches have been developed to jointly analyze longitudinal biomarkers and a time-to-event outcome, most of these methods can only handle one or a few biomarkers. In this article, we propose a novel joint latent class model to deal with high dimensional longitudinal biomarkers. Our model has three components: a class membership model, a survival submodel, and a longitudinal submodel. In our model, we assume that covariates can potentially affect biomarkers and class membership. We adopt a penalized likelihood approach to infer which covariates have random effects and/or fixed effects on biomarkers, and which covariates are informative for the latent classes. Through extensive simulation studies, we show that our proposed method has improved performance in prediction and assigning subjects to the correct classes over other joint modeling methods and that bootstrap can be used to do inference for our model. We then apply our method to a dataset of patients with idiopathic pulmonary fibrosis, for whom gene expression profiles were measured longitudinally. We are able to identify four interesting latent classes with one class being at much higher risk of death compared to the other classes. We also find that each of the latent classes has unique trajectories in some genes, yielding novel biological insights.